![]() “These models tell you something that is quite unique in their form. They also factor in individual behavior, such as vaccine usage or mask-wearing, which is traditionally difficult to account for, yet is a huge factor in disease spread. These models simulate realistic populations, complete with a proper distribution of age, gender, ethnicity, job type, and geographical location. They lose all aspects of heterogeneity and asymmetry that exist in the real world.”Īgent-based modeling eliminates this oversight by representing individual people moving throughout their specific environment, behaving as normal humans would behave. Compartmental mass-action models treat everyone as identical. Marathe, “if I want to understand how a disease spreads in a population, then I want to understand the social network that underlies this population because I want to capture the underlying heterogeneity that exists. Pioneered by Marathe and his group in 2004, agent-based models take into account real-world factors, such as geography, social relationships, and individual differences-things that can have a major impact on how a disease spreads through a community but are lost in traditional epidemiology models. Agent-based modeling is a relative newcomer to the party. In the realm of epidemiology, three main types of modeling are used, each with its strengths and limitations: statistical-based models, compartmental mass-action models, and agent-based models. But even with the help of supercomputers, not all datasets are created equal-the type of model you use to obtain the data matters. Without computational epidemiology, pandemic-response teams and decision-makers would be flying blindfolded. Models and algorithms can be used to evaluate various intervention strategies, including pharmaceutical interventions such as vaccinations and anti-virals, as well as non-pharmaceutical interventions such as social distancing and school closures.” “Computational models help in understanding the space-time dynamics of epidemics. ![]() The importance of computational epidemiology during a pandemic cannot be overstated-more data equates to more accurate projections, and high-performance computing (HPC) is needed to run the simulations and analyze that data quickly. The scenario models produced by the team were not only used by local, state, and federal officials to help make informed decisions regarding pandemic response, but were also used to bolster the CDC’s COVID-19 Scenario Modeling Hub, a consortium combining long-term COVID-19 projections from different research teams. In this instance, the NSSAC team was tasked with simulating scenarios surrounding the COVID-19 pandemic. He and his team use advanced modeling techniques and simulations to investigate large-scale biological, social, and technological systems. Madhav Marathe, the Director of the Network Systems Science and Advanced Computing (NSSAC) division of the Biocomplexity Institute and Initiative at the University of Virginia, works in the field of computational epidemiology. The computational power of Anvil was used to run these scenario models, which produced data that helped drive pandemic response efforts in real-time at varying levels of government.ĭr. The team used computer models to predict what might happen with the virus, such as how it might spread, how many people could get sick and need to go to the hospital, and how vaccines might help over time. A research group from the University of Virginia (UVA) utilized Purdue’s Anvil supercomputing cluster to help provide COVID-19 scenario modeling for local, state, federal, and university officials and departments.
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